prime-grokking.git - Prime Grokking — can minimal architectures grok next-prime? Pre-registered negative-class experiment (weight-tied RNN vs transformer).

Age	Commit message (Collapse)	Author
11 days	fix(eval): correct sieve-rank indexing + test expectations	Void Agent
	- _compute_sieve_rank: find LARGEST k (not smallest) whose signature contains all error preds — fixes P5(4) vs P5(1) bug - _sieve_rank_signature: iterate all small_primes (not just p²≤n) to catch composites like 209=11*19 where 11²>209 - Test expectations updated for [101,200] range (209 outside it) - is_prime task: P1→P3 (no sieve-rank for classification tasks) 48/48 green.
11 days	fix(eval): P5/P6 sieve-rank ladder replaces hardcoded FLAGGED_SIEVE_PREDS	Void Agent
	- probe_report: dynamic lo/hi (no more hardcoded [101,200]) - main(): probe range derived from range_end ([range_end+1, range_end+1000]) - P6 = exact (no probe misses), P5(k) = errors match rank-k sieve signature - _sieve_rank_signature: computes composites with all factors > p_k - _compute_sieve_rank: identifies rank from model's error predictions - build_report.py: probe_fig uses sieve rank for coloring - Tests updated for P5/P6 codes (48/48 green)
2026-08-14	eval: P1 diagnostic prediction-based (flagged set +209), classifier ↵	Void Agent
	regression suite (10 tests, stub ground truth); 34 tests green
2026-08-14	fix codex BLOCKERs: global fixed layout invariance, min_steps off-by-one, ↵	Void Agent
	integers EOS alias, rerun guard, eval prereg-literal codes + dual-checkpoint honesty; +7 regression tests
2026-08-14	implement Experiment 1: data pipeline, tied-RNN w/ ACT halting, transformer ↵	Void Agent
	baseline, train/eval/plot, 16 tests